Method for identifying 3D model based on sparse coding

Abstract

The invention discloses a method for identifying a 3D model based on sparse coding. The method comprises the following steps: acquiring a set of 2D views of different 3D models to construct a database; selecting a checking 3D model to obtain low-layer features; using a sparse coding model to learn a filter function between low-layer features and primary high-layer features; obtaining low-layer features of a comparing 3D model from the database; based on the filter function, conducting convolution on the low-layer features of the comparing 3D model, obtaining high-layer features of the comparing 3D model through pooling; based on the filter function, conducting convolution on a 2D view of the comparing 3D model, obtaining high-layer features of the checking 3D model through pooling; using high-layer features of the 2D view, calculating similarity between the checking model and the comparing model; determining whether all 3D models in the database are taken as comparing 3D models; arranging similarities between the checking 3D model and all the comparing 3D models, taking the type of the 3D model which has the highest similarity as an identification result. According to the invention, the method greatly increases identification effect.

Description

technical field [0001] The invention relates to a three-dimensional model recognition method. In particular, it relates to a 3D model recognition method based on sparse autoencoding. Background technique [0002] In recent years, with the development of 3D modeling tools, the development of 3D reconstruction technology, and the continuous improvement of computer image processing capabilities, 3D object models have been widely used in different fields, such as: cultural heritage [1] , computer aided design (CAD), computer vision, medical imaging [2] , 3D printing, entertainment [3] Wait. Compared with other multimedia information, the 3D model and the virtual reality scene generated by it can provide richer feature information and more realistic stereoscopic vision characteristics. These advantages make it the fourth multimedia data type after sound, image and video [4] At the same time, more and more researchers began to join in the related research work of 3D model. ...

AI Technical Summary

Problems solved by technology

[0005] The main challenge currently faced in the field of content-based 3D model recognition is: after the 3D model is converted into a set of 2D views, although the existing feature descriptors are rich and diverse, most of them can only describe the primary features of a single aspect of the picture. And the redundancy between the data is large

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